Here is a powerful example of numpy diff function. In this example, I chunk of memory and I need to find start, end, and length of the memory. 1 means allocated and 0 means free. using numpy.diff I was able to find start and the end of the allocated memory. Another example thing I use here is dynamic size of the column col_size = 7 and the power…

numpy_diff_and_formatting.py

import numpy as np

# Memory allocation:
# '1' means allocated, '0' means free
memory = np.array([0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0])
# contiguous memory allocations
diff = np.diff(memory)
# starts indices
starts = np.nonzero(diff == 1)[0] + 1
# ends indices
ends = np.nonzero(diff == -1)[0]
sizes = ends - starts
indices = np.argsort(sizes)

# printing column width
col_size = 7
print(f"{'starts':>{col_size}}{'ends':>{col_size}}{'size':>{col_size}}")
for i in range(len(sizes)):
    # example on how to break long print column by multiple lines
    # also example on how to use variable column size in print format
    print(
        f"{starts[indices[i]]:>{col_size}}"
        f"{ends[indices[i]]:>{col_size}}"
        f"{sizes[indices[i]] + 1:>{col_size}}"
    )

# output
#  starts   ends   size
#       9      9      1
#       1      2      2
#       5      7      3